小黑山岛海域刺参、魁蚶和紫贻贝生境适宜性分析

以小黑山岛临近海域为研究对象,利用生境适宜性指数(habitat suitability index,HSI)模型选划适宜刺参(Stichopus japonicas)、魁蚶(Scapharca broughtonii)和紫贻贝(Mytilus edulis)增殖修复的区域。分别针对每个修复物种筛选出7个生境评价因子,结合专家赋值法和层次分析法确定每个评价因子的权重,利用GIS空间分析模块将现状调查数据进行插值、重分类和栅格计算,绘制研究区域目标种群生境适宜性地图。结果表明:对于刺参和紫贻贝,研究区域均适宜其生长繁殖,同一物种,相同季节在空间上无站位差异,但各季节的生境适宜性分区变化明显;对于魁蚶来说,东北部海域较适宜增殖,其次为西部海域,四季均以较适宜生境为主,仅冬季出现基本适宜生境。水温是造成季节差异的主要因素,底质类型则是引起生境站位差异的重要原因。可为后续的生物多样性保育和生态修复提供基础资料参考。     

新疆塔什库尔干野生动物自然保护区马可波罗盘羊生境适宜性评价

马可波罗盘羊是帕米尔高原的代表性物种,开展生境适宜性评价对于该物种的保护与管理具有重要意义。综合考虑植被、地形等影响马可波罗盘羊生境选择的关键因子,以及道路和牧场等人为干扰因子,借助ArcGIS,构建生境适宜性指数模型,在不同尺度上评价分析马可波罗盘羊的生境适宜性及其季节变化特征。结果表明:15836 km~2保护区内马可波罗盘羊夏季潜在适宜生境面积为2127.19 km~2,冬季为1915.70 km~2。保护区西北部面积3767.73 km~2的马可波罗盘羊实际分布区内,夏季潜在适宜生境面积为1095.48 km~2,冬季为1072.82 km~2,马可波罗盘羊适宜生境集中分布于保护区内该物种实际分布区。受人为干扰,保护区内马可波罗盘羊夏季和冬季实际适宜生境丧失率分别为18.43%和17.78%,实际分布区内适宜生境丧失率分别为33.65%和29.73%,表明实际分布区马可波罗盘羊适宜生境受人类活动影响较大,应予以重点保护。总体而言,影响马可波罗盘羊生境适宜性的关键因素是国道314和放牧。基于上述研究结果,作者提出了4点关于马可波罗盘羊生境保护,以及保护区管理与规划的建议与思考。     

亚洲象生境长时序变化及其对人象冲突的影响——基于融合MaxEnt与HSI模型的生境适宜性评价方法

生境变化直接关系到物种种群维持与人类安全,揭示其长期变化规律及其对人类的影响,可为物种保护与生境恢复提供科学支撑。但因受物种活动点数据获取与位置精度的局限,鲜见濒危、危险物种的长时序生境变化研究。以人象冲突频发的西双版纳勐海—普洱澜沧地区亚洲象种群(勐海—澜沧象群)活动区为例,提出融合MaxEnt与HSI模型的亚洲象长时序生境适宜性评价方法,即基于荟萃分析筛选出的15个亚洲象生境评价因子,结合近期有限的物种活动点监测数据,利用MaxEnt得到生境评价因子的贡献率,再运用HSI模型计算生境适宜性指数;利用该方法制作出研究区1988—2020年逐年时序的亚洲象生境适宜性图,以分析亚洲象生境的时空变化,将其与亚洲象肇事数据结合,进而分析人象冲突与生境变化的关联。结果表明：(1)基于物种生境偏好不变的前提,融合MaxEnt模型与HSI模型的生境适宜性评价方法可应用于物种的长时序生境评价,且基于亚洲象活动点数据从动物对生境利用的生态学视角定量获取亚洲象对各生境评价因子的偏好程度,使生境评价结果具有良好的生态可解释性;(2)目前亚洲象适宜生境面积占研究区面积三分之一(4039.76 km^...     

元江-李仙江流域亚洲象生境适宜性评价——基于荟萃分析和遥感大数据分析

生境适宜性评价是物种保护和生境管理与规划的基础。近几十年来，云南境内野生亚洲象数量剧增，外扩迁移事件频发，而新迁入区域生境适宜状况因物种出现点数据缺乏而难以评价，掣肘迁移亚洲象保护与风险防范应急。以亚洲象新近迁入的元江-李仙江流域为案例区，采用荟萃分析统计亚洲象生境评价因子，结合相关性分析和方差膨胀因子独立性检验，筛选出生境评价因子；基于开源遥感数据产品量化生境因子，综合主成分分析和层次分析计算生境评价因子权重，采用生境适宜性指数(Habitat Suitability Index, HSI)模型评价元江-李仙江流域亚洲象生境适宜性，并分析其景观格局。结果表明：(1)元江-李仙江流域亚洲象生境适宜性空间格局主要表现为由下游至上游呈递减趋势，最适生境主要分布于流域下游段，而流域上游段适宜生境少；(2)元江流域生境适宜性低于李仙江流域，且其生境斑块连接度更低、破碎化更严重；(3)2021年“北移象群”北迁沿程生境适宜性由西南向东北呈下降趋势。基于亚洲象生境适宜性评价结果，科学引导野生亚洲象迁入适宜生境区，以规避人象冲突，保障外迁亚洲象群及其活动区居民生命财产安全，服务于区域生物多样性保护与...     

基于生态位模型的艾比湖国家级自然保护区马鹿生境评价

生境评价和预测是对濒危物种进行有效保护的基础。通过2013年9月和2014年10月对新疆艾比湖国家级自然保护区开展2次秋季野外调查共收集了92处马鹿(Cervuselaphus)出现数据,利用马鹿出现数据作为分布点数据,选取地形、植被类型和气候因子3类23种因子作为生境变量,利用MAXENT生态位模型分析了新疆艾比湖国家级自然保护区马鹿秋季生境适宜性分布特征和主要生境因子对马鹿分布的影响。结果表明:模型预测结果较高,平均AUC(area under the curve,受试工作者曲线下面值)值为0.976;Jackknife检验结果显示:最热月最高温度对马鹿生境分布的影响较大。植被类型和坡度对马鹿生境分布的影响不大。海拔、年降雨量、气温日较差和最热季平均温度是影响马鹿生境分布的主要生境因子。马鹿秋季生境划分为高适宜、次适宜、低适宜和不适宜4个等级,马鹿的高适宜生境区主要分布在研究区域的北部,次适宜及低适宜生境区则分布于高适宜生境区的边缘,而不适宜生境区主要集中在西部和东部地区。研究不仅提供了马鹿在艾比湖的实际分布状况,也为马鹿生境和生境因子的关系方面提供了一个重要的科学依据。     

基于MAXENT模型的古田山保护区黑麂生境适宜性评价

浙江开化古田山国家级自然保护区是黑麂（Muntiacus crinifrons）的集中分布区之一。近年来,保护区内黑麂面临着生境丧失和破碎化的威胁。本研究应用MAXENT模型,结合古田山保护区2014-2017年的红外相机监测数据和主要环境变量数据,对保护区内黑麂生境适宜性的季节变化特征及影响因素进行了评价与分析。结果表明：距阔叶林距离、海拔两个变量对黑麂生境适宜性的季节性变化影响最为显著。古田山保护区不同季节黑麂的适宜生境面积为：春季2086.38 hm²、夏季2608.74 hm²、秋季2502.27 hm²和冬季1746.27 hm²,分别占保护区总面积的25.74%、32.18%、30.87%和21.54%。从空间分布来看,黑麂适宜生境主要分布在保护区的核心区和北部区域。建议加强对保护区的核心区和北部区域自然植被的保护与恢复,以及对保护区人为干扰活动的监督和管理。     

基于多尺度的丹顶鹤生境适宜性评价——以黄河三角洲自然保护区为例

尺度是生态学中的一个核心问题,基于多尺度更能抓住物种一环境之间关系.生境适宜性模型可以定量并多尺度研究物种一环境关系,被广泛应用于野生动物生境适宜性评价中.本文以丹顶鹤(Grus japonensis)为研究对象,以其迁徙和越冬的重要地区--黄河三角洲自然保护区为研究区域,应用二项逻辑斯谛回归模型,并结合地理信息系统和遥感技术,在10-1,500 ha之间,通过变换空间尺度大小构建了10个空间尺度下丹顶鹤生境适宜性模型.通过检测尺度对模型构建的影响,选择最佳模型开展丹顶鹤生境适宜性分析和评价.尺度影响分析结果表明:环境因子的拟合能力和模型的预测精度均存在尺度效应,空间尺度为50 ha时的单尺度模型为最佳单尺度模型,多尺度模型优于所有单尺度模型.模型分析结果表明:丹顶鹤适宜生境占保护区总面积的25%以上,且大部分适宜生境分布在自然保护区南部,自然保护区北部由于缺乏淡水来源,适宜生境较少.为有效保护丹项鹤生境,建议加强保护区湿地生境监测、评价和规划.以及对人为干扰活动进行监督和管理.     

基于模糊数学的秦岭地区山茱萸生境适宜性评价

山茱萸(Cornus officinalis)是我国传统常用药材,本文采用模糊数学分析方法,对采自秦岭地区的山茱萸中马钱苷含量与21个评价因子的隶属函数进行拟合,同时采用最大信息熵模型确定各个评价因子的权重,利用ArcGIS 10空间分析模块模拟研究区域适宜山茱萸生长的潜在分布生境。结果表明,在山茱萸生境的21个评价因子中,主要影响因子为气候,其次是土壤和地形因子;所有评价因子中,土壤质地(TTEX)的权重最大,其次是果实生长期降水量(PG)、年降水量(AP)和降水季节性变化(PS)。研究区内山茱萸高适宜区面积占总面积的19.94%,主要分布在甘肃东南部、陕西南部和河南西部,这些区域温度适中、气候湿润、光照充足,适宜山茱萸生长;适宜区面积占总面积的11.85%,低适宜区面积占总面积的16.31%,不适宜区面积占总面积的51.90%。本研究基于GIS与模糊数学的生境适宜性评价模型,对秦岭地区山茱萸生境适宜性做出了科学划分,同时量化了不同生境区的评价因子对山茱萸的影响,可为山茱萸的管理和保护以及人工种植提供科学依据。     

扎龙湿地丹顶鹤繁殖生境质量变化

基于1996和2004年扎龙湿地丹顶鹤生境因子专题图,通过建立生境适宜性模型和种群格局最邻近体模型,定量分析了扎龙湿地丹顶鹤繁殖生境质量的变化.结果表明：研究期间,扎龙湿地丹顶鹤繁殖适宜生境经历了面积丧失和功能丧失过程;2004年,研究区内丹顶鹤繁殖适宜性生境已大量丧失,核心区繁殖适宜生境已经严重斑块化.丹顶鹤繁殖生境选择行为对生境质量变化的响应表现为两个过程：一是丹顶鹤巢址不断向核心区集中的过程,二是在核心区的分布格局经历了从均匀分布到成群分布的生态过程.     

基于MaxEnt模型的秦岭山地斑羚生境适宜性评价

生境适宜性评价对野生动物的保护与管理具有重要意义。为了解陕西秦岭地区斑羚(Naemorhedus griseus)的生境状况,利用2011—2013年间在秦岭地区采集的斑羚分布点数据,通过MaxEnt模型对陕西秦岭地区的斑羚生境进行适宜性评价。结果表明,陕西秦岭地区的斑羚适宜生境面积约为9800 km~2,占秦岭山地面积的17%,主要位于秦岭中西部区域;次适宜生境面积约为6940 km~2,占秦岭面积的12%,主要位于适宜生境的周边区域。海拔、月均昼夜温差和年降雨量是影响陕西秦岭地区斑羚生境适宜性的主要环境变量,而人类干扰对生境适宜性的影响较小。陕西秦岭地区的斑羚偏好于选择1800—3000 m的中高海拔区间、年降水量为750—850 mm、月均昼夜温差8℃左右的环境。明确了斑羚适宜生境在秦岭的分布状况及关键环境影响因子,可为下一步制定濒危动物保护和生境管理提供理论依据。     

唐家河国家级自然保护区川金丝猴生境适宜性评价

生境适宜性评价是濒危物种保护的重要基础。川金丝猴（Rhinopithecus roxellana）是栖息于温带森林的、中国特有的珍稀灵长类动物。位于岷山山系的四川唐家河国家级自然保护区是川金丝猴的重要分布地之一,但涉及该地区川金丝猴的生境信息却较缺乏。运用最大熵（Maximum entropy,MaxEnt）模型对四川唐家河国家级自然保护区川金丝猴不同季节的生境适宜性进行了研究,发现四个季节的训练集和验证集的受试者工作特征曲线下面积（Area under the receiver operator characteristic curve,AUC）值均超过0.8,说明模型预测结果较好。结果显示：（1）影响不同季节川金丝猴分布的主要因子是海拔、河流和道路。（2）川金丝猴的适宜生境面积存在季节性变化。其中,春季的适宜生境面积最大,为233.94 km²,占全区面积的58.48%;夏季的次之,为192.75 km²,占48.19%;秋冬季的适宜生境面积相对较低,分别为145.54 km²（占36.39%）和142.63 km²（占35.66%）。（3）川金丝猴的适宜生境分布具有明显的季节性垂直变化。研究揭示保护好完整的森林植被带对川金丝猴的生存具有重要意义,尤其要重视对人为干扰较强的低海拔生境的保护。     

马鞍列岛褐菖鲉Sebasticus marmoratus栖息地适宜性评价

为了评估趋礁鱼类在岛礁海域的生境适宜度,选取马鞍列岛的褐菖鲉(Sebasticus marmoratus)为指示物种,以2009年获取的水深、盐度、叶绿素a、浊度和底质数据作为褐菖鲉春、冬季栖息地指示因子,建立栖息地适宜度曲线,并计算各站点的栖息地适宜性指数(HSI)。结果显示:1)绿华、花鸟、嵊山沿岸站点HSI普遍较低,枸杞岛、三横山、东库山沿岸站点褐菖鲉HSI相对较高,其中最大值1.0出现在枸杞岛沿岸的站点;2)春季褐菖鲉幼鱼的适宜水深在6 m左右,成鱼适宜在8—12 m的水深处生存;冬季褐菖鲉对8—12 m的水深适宜性良好;3)春季所有褐菖鲉的适宜盐度为30PSU,冬季幼鱼的适宜盐度为27—31PSU,成鱼的适宜盐度为27PSU、31PSU;4)随着叶绿素a和浊度值的增大,褐菖鲉适宜性逐渐降低。底质类型为岩时最适合褐菖鲉生存。5)相关分析显示,褐菖鲉丰度与底质类型相关性最大,而与叶绿素a、浊度呈显著负相关。研究结果表明,初级生产力和浑浊程度越高对褐菖鲉丰度抑制越明显。底质类型是褐菖鲉丰度分布的重要影响因子,其中分布有较多大型海藻的岩礁生境是其最适宜的栖息地。利用2010年春、冬季环境调查和渔获数据进行HSI模型验证,资源丰度随HSI值升高而增加,因此构建的模型可用于趋礁鱼类在岛礁海域的栖息地适宜性分析。     

Evaluation of Habitat Suitability Models for Forest Passerines Using Demographic Data

ABSTRACT Habitat suitability is often used as a surrogate for demographic responses (i.e., abundance, survival, fecundity, or population viability) in the application of habitat suitability index (HSI) models. Whether habitat suitability actually relates to demographics, however, has rarely been evaluated. We validated HSI models of breeding habitat suitability for wood thrush (Hylocichla mustelina) and yellow-breasted chat (Icteria virens) in Missouri, USA. First, we evaluated HSI models as a predictor of 3 demographic responses: within-site territory density, site-level territory density, and nest success. We demonstrated a link between HSI values and all 3 types of demographic responses for the yellow-breasted chat and site-level territory density for the wood thrush. Second, we evaluated support for models containing HSI values, models containing measured habitat features (e.g., tree age, tree species, ecological land type), and models containing management treatments (e.g., even-aged and uneven-aged forest regeneration treatments) for each demographic response using model selection. Models containing HSI values received more support, in general, than models containing only habitat features or management treatments for all 3 types of wildlife response. The assumption that changes in habitat suitability represent wildlife demographic response to vegetation change is supported by our models. However, differences in species ecology may contribute to the degree to which HSI values are related to specific demographic responses. We recommend validation of HSI models with the particular demographic data of interest (i.e., density, productivity) to increase confidence in the model used for conservation planning.     

Using Bayesian networks to incorporate uncertainty in habitat suitability index models

Habitat suitability index (HSI) models rarely characterize the uncertainty associated with their estimates of habitat quality despite the fact that uncertainty can have important management implications. The purpose of this paper was to explore the use of Bayesian belief networks (BBNs) for representing and propagating 3 types of uncertainty in HSI models—uncertainty in the suitability index relationships, the parameters of the HSI equation, and measurement of habitat variables (i.e., model inputs). I constructed a BBN–HSI model, based on an existing HSI model, using Netica™ software. I parameterized the BBN's conditional probability tables via Monte Carlo methods, and developed a discretization scheme that met specifications for numerical error. I applied the model to both real and dummy sites in order to demonstrate the utility of the BBN–HSI model for 1) determining whether sites with different habitat types had statistically significant differences in HSI, and 2) making decisions based on rules that reflect different attitudes toward risk—maximum expected value, maximin, and maximax. I also examined effects of uncertainty in the habitat variables on the model's output. Some sites with different habitat types had different values for E[HSI], the expected value of HSI, but habitat suitability was not significantly different based on the overlap of 90% confidence intervals for E[HSI]. The different decision rules resulted in different rankings of sites, and hence, different decisions based on risk. As measurement uncertainty in habitat variables increased, sites with significantly different (α = 0.1) E[HSI] became statistically more similar. Incorporating uncertainty in HSI models enables explicit consideration of risk and more robust habitat management decisions. © 2012 The Wildlife Society.     

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low‐quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision‐making framework will result in better‐informed, more robust decisions.     

Predicting global habitat suitability for stony corals on seamounts

Aim Globally, species distribution patterns in the deep sea are poorly resolved, with spatial coverage being sparse for most taxa and true absence data missing. Increasing human impacts on deep‐sea ecosystems mean that reaching a better understanding of such patterns is becoming more urgent. Cold‐water stony corals (Order Scleractinia) form structurally complex habitats (dense thickets or reefs) that can support a diversity of other associated fauna. Despite their widely accepted ecological importance, records of scleractinian corals on seamounts are patchy and simply not available for most of the global ocean. The objective of this paper is to model the global distribution of suitable habitat for stony corals on seamounts. Location Seamounts worldwide. Methods We compiled a database containing all accessible records of scleractinian corals on seamounts. Two modelling approaches developed for presence‐only data were used to predict global habitat suitability for seamount scleractinians: maximum entropy modelling (Maxent) and environmental niche factor analysis (ENFA). We generated habitat‐suitability maps and used a cross‐validation process with a threshold‐independent metric to evaluate the performance of the models. Results Both models performed well in cross‐validation, although the Maxent method consistently outperformed ENFA. Highly suitable habitat for seamount stony corals was predicted to occur at most modelled depths in the North Atlantic, and in a circumglobal strip in the Southern Hemisphere between 20° and 50° S and shallower than around 1500 m. Seamount summits in most other regions appeared much less likely to provide suitable habitat, except for small near‐surface patches. The patterns of habitat suitability largely reflect current biogeographical knowledge. Environmental variables positively associated with high predicted habitat suitability included the aragonite saturation state, and oxygen saturation and concentration. By contrast, low levels of dissolved inorganic carbon, nitrate, phosphate and silicate were associated with high predicted suitability. High correlation among variables made assessing individual drivers difficult. Main conclusions Our models predict environmental conditions likely to play a role in determining large‐scale scleractinian coral distributions on seamounts, and provide a baseline scenario on a global scale. These results present a first‐order hypothesis that can be tested by further sampling. Given the high vulnerability of cold‐water corals to human impacts, such predictions are crucial tools in developing worldwide conservation and management strategies for seamount ecosystems.